High speed serial links operating at over 3 gigabits per second (Gbs) over distances in excess of several feet using only copper traces on conventional FR-4 dielectric printed circuit board (PCB) electrical backplanes have become commonplace. In fact, transceivers operating at rates in excess of 6 Gbs over similar PCB-based serial links are now becoming available in the marketplace. It is expected that rates of 10 Gbs will soon be introduced.
Such serial links commonly employ a serializer deserializer (SERDES) for multiplexing and demultiplexing multiple high speed data streams. As the bit rates (and concomitant frequencies) in these applications have escalated over time, system designers have had to contend with the difficulty of communicating over these increasingly dispersive links without concomitant sacrifice in system performance. The current trend in the design of these transceivers is to gravitate towards techniques more commonly encountered in digital communications system design: increased reliance on signal processing and statistical system characterization. One of the more prominent examples of this design philosophy is evident in the application of equalization to combat the increased frequency selectivity of the channel.
The decision feedback equalizer (DFE) (see, Austin, “Decision-Feedback Equalization for Digital Communication Over Dispersive Channels,” MIT Lincoln Laboratory, Tech. Report No. 437, August 1967, incorporated herein by reference) has become very popular in communications system design due to its effectiveness under a wide variety of channel types. This nonlinear equalizer is especially effective on channels with severe dispersion, because it can correct for channel imperfections without displaying the excessive noise enhancement of a linear equalizer.
A DFE has a precursor (or feedforward) equalizer, F(z), and a postcursor (or feedback) equalizer, B(z). The precursor equalizer is a linear transversal filter, the purpose of which is to cancel precursor intersymbol interference (ISI). The precursor equalizer does this by filtering the channel output, attempting to relocate most of the channel precursor energy to the postcursor response of the filtered P(z)F(z).
The postcursor equalizer, B, is strictly causal (bi=0 for i ∈{−∞, . . . , 0}). B uses past decisions to cancel the remaining postcursor ISI from the current decision variable. A nonlinear symbol-rate slicer located in the feedback loop performs this cancellation and accounts for the nonlinear behavior of the DFE.
In a classical DFE implementation, F and B are both adapted to P by an adaptation algorithm based on one of several possible criteria. Two well-known criteria are zero forcing and minimum mean squared error. A popular adaptation algorithm is the least mean squared (LMS) algorithm or one of its variants (see, Proakis, Digital Communications, 3rd ed., New York: McGraw-Hill, 1995).
Adaptation of the equalizers F and B to P can occur either in “trained” or “blind” modes. The trained mode calls for the transmitter to send a symbol sequence that is known to the receiver. The receiver then substitutes the known “perfect” sequence for the (possibly incorrect) detected sequence during adaptation (usually when the physical layer link is established or channel conditions change). The blind mode is characterized by the absence of any training data. Adaptation proceeds solely on receiver decisions.
The equalizers F and B operate at the symbol rate (F may operate at some integer multiple of the symbol rate). In high speed serial links this can be rather challenging to implement for a variety of reasons. In particular, digital transversal filters typically require the use of high-speed analog-to-digital converters (ADCs) with many bits of resolution, which are difficult to design for gigabit rates. Analog implementation may require use of analog delay lines, which are also difficult to design for gigabit rates.
To circumvent some of these and other difficulties, it is conventional to augment the transmitter with either postcursor or precursor cancellation. Postcursor cancellation is generally known as Tomlinson-Harashima preceding (see, Forney, Jr, et al., “Combined Equalization and Coding Using Precoding,” IEEE Communications Magazine, Vol. 29, No. 12, pp. 25-34, December 1991, incorporated herein by reference) and is often motivated more by concerns about error propagation. Precursor cancellation is often referred to as “preemphasis.”
Unfortunately, precursor cancellation at the transmitter often creates significant problems at the receiver. Precursor cancellation at the transmitter renders adaptive derivation of filter coefficients virtually impossible unless the link is bidirectional. This is because, while it may be possible to measure P at the receiver (using a training sequence) and thus derive F, no feedback mechanism (or “back channel”) exists to communicate the derived F back to the transmitter. Thus F may have to be fixed for a particular channel.
Given knowledge of P, F can be selected based on a priori knowledge of the particular phase to which the CR circuit in the receiver should lock. This computation may be based on knowledge of the specific criteria used by the CR circuit to acquire lock. For example, the CR circuit can be designed to converge to the specific phase that minimizes the residual amplitude at the crossing point between two successive symbols of equal amplitude but of opposite polarity.
The practical reality of this situation, though, is that the CR circuit might not always converge to and maintain lock at the exact optimum phase for which it was designed. This results in at least two kinds of offsets. First, even at steady state, the instantaneous phase will exhibit some fluctuation about the time-averaged value. Second, some difference will always be evident between the phase at which the CR circuit should lock and the phase at which it actually does lock due to a myriad of possible design-related concessions. If the two phases differ significantly from one another, the result will almost certainly be a degradation of the symbol error rate (SER) due to misequalization. This is because the two phases require different “F”s. The postcursor equalizer B may be able to compensate to some extent for any residual postcursor intersymbol interference (ISI) introduced by using a different F, but no such relief exists where residual precursor ISI is concerned.
Accordingly, what is needed in the art is a better way to perform precursor equalization. What is further needed in the art is a SERDES that incorporates a better way to select precursor equalizer coefficients.